A Skull-Adaptive Framework for AI-Based 3D Transcranial Focused Ultrasound Simulation
Journal:
arXiv
Published Date:
May 19, 2025
Abstract
Transcranial focused ultrasound (tFUS) is an emerging modality for
non-invasive brain stimulation and therapeutic intervention, offering
millimeter-scale spatial precision and the ability to target deep brain
structures. However, the heterogeneous and anisotropic nature of the human
skull introduces significant distortions to the propagating ultrasound
wavefront, which require time-consuming patient-specific planning and
corrections using numerical solvers for accurate targeting. To enable
data-driven approaches in this domain, we introduce TFUScapes, the first
large-scale, high-resolution dataset of tFUS simulations through anatomically
realistic human skulls derived from T1-weighted MRI images. We have developed a
scalable simulation engine pipeline using the k-Wave pseudo-spectral solver,
where each simulation returns a steady-state pressure field generated by a
focused ultrasound transducer placed at realistic scalp locations. In addition
to the dataset, we present DeepTFUS, a deep learning model that estimates
normalized pressure fields directly from input 3D CT volumes and transducer
position. The model extends a U-Net backbone with transducer-aware
conditioning, incorporating Fourier-encoded position embeddings and MLP layers
to create global transducer embeddings. These embeddings are fused with U-Net
encoder features via feature-wise modulation, dynamic convolutions, and
cross-attention mechanisms. The model is trained using a combination of
spatially weighted and gradient-sensitive loss functions, enabling it to
approximate high-fidelity wavefields. The TFUScapes dataset is publicly
released to accelerate research at the intersection of computational acoustics,
neurotechnology, and deep learning. The project page is available at
https://github.com/CAMMA-public/TFUScapes.
